from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-19 14:07:33.450200
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 19, Nov, 2020
Time: 14:07:36
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.2786
Nobs: 115.000 HQIC: -43.5549
Log likelihood: 1175.94 FPE: 5.09801e-20
AIC: -44.4268 Det(Omega_mle): 2.40708e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.814325 0.220301 3.696 0.000
L1.Burgenland 0.143282 0.092375 1.551 0.121
L1.Kärnten -0.321701 0.077577 -4.147 0.000
L1.Niederösterreich -0.001863 0.223163 -0.008 0.993
L1.Oberösterreich 0.283029 0.180528 1.568 0.117
L1.Salzburg 0.115721 0.091228 1.268 0.205
L1.Steiermark 0.054912 0.129610 0.424 0.672
L1.Tirol 0.165635 0.085221 1.944 0.052
L1.Vorarlberg 0.012173 0.085523 0.142 0.887
L1.Wien -0.224171 0.176629 -1.269 0.204
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.818179 0.285035 2.870 0.004
L1.Burgenland -0.015293 0.119519 -0.128 0.898
L1.Kärnten 0.340893 0.100373 3.396 0.001
L1.Niederösterreich 0.058156 0.288739 0.201 0.840
L1.Oberösterreich -0.208327 0.233576 -0.892 0.372
L1.Salzburg 0.149451 0.118036 1.266 0.205
L1.Steiermark 0.171736 0.167696 1.024 0.306
L1.Tirol 0.141942 0.110262 1.287 0.198
L1.Vorarlberg 0.192376 0.110654 1.739 0.082
L1.Wien -0.612613 0.228531 -2.681 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.355015 0.094670 3.750 0.000
L1.Burgenland 0.103881 0.039696 2.617 0.009
L1.Kärnten -0.024144 0.033337 -0.724 0.469
L1.Niederösterreich 0.127442 0.095900 1.329 0.184
L1.Oberösterreich 0.263068 0.077578 3.391 0.001
L1.Salzburg -0.000684 0.039203 -0.017 0.986
L1.Steiermark -0.064116 0.055697 -1.151 0.250
L1.Tirol 0.094979 0.036622 2.594 0.009
L1.Vorarlberg 0.148317 0.036752 4.036 0.000
L1.Wien 0.006521 0.075903 0.086 0.932
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.220426 0.113769 1.937 0.053
L1.Burgenland 0.003985 0.047705 0.084 0.933
L1.Kärnten 0.036016 0.040063 0.899 0.369
L1.Niederösterreich 0.089012 0.115248 0.772 0.440
L1.Oberösterreich 0.349681 0.093230 3.751 0.000
L1.Salzburg 0.092429 0.047113 1.962 0.050
L1.Steiermark 0.190658 0.066934 2.848 0.004
L1.Tirol 0.026238 0.044010 0.596 0.551
L1.Vorarlberg 0.114673 0.044166 2.596 0.009
L1.Wien -0.122552 0.091216 -1.344 0.179
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.958121 0.242920 3.944 0.000
L1.Burgenland 0.041631 0.101859 0.409 0.683
L1.Kärnten -0.017222 0.085542 -0.201 0.840
L1.Niederösterreich -0.145132 0.246076 -0.590 0.555
L1.Oberösterreich 0.052885 0.199063 0.266 0.790
L1.Salzburg 0.046213 0.100595 0.459 0.646
L1.Steiermark 0.095314 0.142918 0.667 0.505
L1.Tirol 0.239007 0.093970 2.543 0.011
L1.Vorarlberg 0.029696 0.094304 0.315 0.753
L1.Wien -0.262171 0.194764 -1.346 0.178
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183694 0.169980 1.081 0.280
L1.Burgenland -0.044130 0.071275 -0.619 0.536
L1.Kärnten -0.010388 0.059857 -0.174 0.862
L1.Niederösterreich 0.206275 0.172189 1.198 0.231
L1.Oberösterreich 0.393747 0.139292 2.827 0.005
L1.Salzburg -0.035653 0.070390 -0.507 0.612
L1.Steiermark -0.051604 0.100005 -0.516 0.606
L1.Tirol 0.196946 0.065755 2.995 0.003
L1.Vorarlberg 0.051388 0.065988 0.779 0.436
L1.Wien 0.120259 0.136284 0.882 0.378
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.350928 0.216458 1.621 0.105
L1.Burgenland 0.055471 0.090764 0.611 0.541
L1.Kärnten -0.083495 0.076224 -1.095 0.273
L1.Niederösterreich -0.149800 0.219271 -0.683 0.494
L1.Oberösterreich -0.120166 0.177379 -0.677 0.498
L1.Salzburg -0.003107 0.089637 -0.035 0.972
L1.Steiermark 0.382738 0.127350 3.005 0.003
L1.Tirol 0.542154 0.083734 6.475 0.000
L1.Vorarlberg 0.218720 0.084031 2.603 0.009
L1.Wien -0.180792 0.173549 -1.042 0.298
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.288153 0.246418 1.169 0.242
L1.Burgenland 0.011838 0.103326 0.115 0.909
L1.Kärnten -0.077348 0.086774 -0.891 0.373
L1.Niederösterreich 0.205505 0.249620 0.823 0.410
L1.Oberösterreich 0.019983 0.201930 0.099 0.921
L1.Salzburg 0.223511 0.102044 2.190 0.028
L1.Steiermark 0.136407 0.144976 0.941 0.347
L1.Tirol 0.057200 0.095324 0.600 0.548
L1.Vorarlberg -0.000629 0.095662 -0.007 0.995
L1.Wien 0.157375 0.197569 0.797 0.426
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.728420 0.136952 5.319 0.000
L1.Burgenland -0.009721 0.057426 -0.169 0.866
L1.Kärnten -0.017549 0.048227 -0.364 0.716
L1.Niederösterreich -0.079713 0.138731 -0.575 0.566
L1.Oberösterreich 0.271833 0.112227 2.422 0.015
L1.Salzburg -0.002567 0.056713 -0.045 0.964
L1.Steiermark -0.004601 0.080573 -0.057 0.954
L1.Tirol 0.081667 0.052978 1.542 0.123
L1.Vorarlberg 0.181359 0.053166 3.411 0.001
L1.Wien -0.133012 0.109803 -1.211 0.226
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.066154 -0.079075 0.188535 0.226289 0.020888 0.069273 -0.169522 0.066505
Kärnten 0.066154 1.000000 -0.072396 0.173114 0.043186 -0.160743 0.174470 -0.010866 0.271063
Niederösterreich -0.079075 -0.072396 1.000000 0.213756 0.031460 0.146410 0.065070 0.031727 0.355851
Oberösterreich 0.188535 0.173114 0.213756 1.000000 0.231310 0.264988 0.068956 0.045206 0.025671
Salzburg 0.226289 0.043186 0.031460 0.231310 1.000000 0.141203 0.035310 0.044546 -0.089160
Steiermark 0.020888 -0.160743 0.146410 0.264988 0.141203 1.000000 0.095277 0.099360 -0.202439
Tirol 0.069273 0.174470 0.065070 0.068956 0.035310 0.095277 1.000000 0.128901 0.087958
Vorarlberg -0.169522 -0.010866 0.031727 0.045206 0.044546 0.099360 0.128901 1.000000 0.048280
Wien 0.066505 0.271063 0.355851 0.025671 -0.089160 -0.202439 0.087958 0.048280 1.000000